During the first two years of the Sportscience site, Frank
Katch contributed a series of original and
insightful articles on history makers in the science of sport and
exercise nutrition. Frank has now officially retired, but he still actively
co-authors the popular McArdle, Katch
and Katch text, Exercise
Physiology: Nutrition, Energy, and Human Performance. On a recent
visit to New Zealand he showed me the proofs of the most recent (8th)
edition, including the introductory chapter on discoveries and developments
in the field of exercise physiology from the time of the ancient Greeks to
the 20th century. At my request he gained the publisher's permission to
provide free access to the PDF. Here is the link: Introduction–A View of the Past. If the
download stalls, see below. You can also access an appendix on landmark publications in exercise
physiology and another on famous female scientists.
The publisher has even offered a generous 20% discount for Sportscience
visitors to purchase the book via this link. Enjoy!

Incredibly, owing to a bug in the Internet Explorer/Adobe
combination, some downloads of some PDFs stall part-way through. Solution:
right-click on the link and Save As… to a convenient location, then open.

Magnitude-based inference (MBI) is the approach to making conclusions
about sample-based effects that we have promoted over the last decade or so (e.g., Batterham and Hopkins, 2006; Hopkins et
al., 2009). In essence, we realized
that a sample never produces the exact value of an effect, but for a given
effect in a given study, the uncertainty might be acceptable. Uncertainty is
represented by the confidence interval or chances of benefit and risk of
harm. An effect with acceptable uncertainty is clear, which means its magnitude is sufficiently well defined to
be worth reporting in various ways that convey the uncertainty in the
magnitude. An effect with too much uncertainty is unclear, and the best conclusion is that you need more data.

Our colleagues who use and understand MBI know that it is
far superior to the traditional approach of null-hypothesis significance
testing, which addresses the question only of whether the true effect could
be null (zero). Generations of statisticians before us have also criticized
the null-hypothesis test, but magnitude-based inference appears to be the
first practical alternative that properly takes into account the uncertainty
arising from sampling variation.

In July this year an article critical of MBI was
e-published ahead of print in Medicine
and Science in Sports and Exercise, authored by Welsh and Knight (2014).We wrote a letter to the editor pointing out
all the mistakes and deficiencies in the article, but the journal has a
policy that letters about articles cannot be published until the article
itself appears in final form in the journal, in this case next April.
Meantime we are hearing from colleagues that reviewers of their manuscripts
are citing Welsh and Knight as sufficient reason to insist on removal of
MBI.

We cannot publish our letter verbatim here, but to
reassure researchers who use MBI and to provide them with something to
address the concerns of reviewers, we are summarizing the most important
points, as follows…

•MBI is definitely not a form of
null-hypothesis significance testing (NHST). In particular, the Type 1 and 2
errors of MBI are conceptually distinct from the Type I and II errors of
NHST. MBI Type 1 is the risk of declaring a marginally harmful effect
beneficial; NHST Type I is the chance of declaring a null effect significant.
MBI Type 2 is the chance of declaring a marginally beneficial effect
non-beneficial; NHST Type II is the chance of declaring a marginally
beneficial effect non-significant.

•The rates of so-called false
discoveries of clear substantial effects, when the true effect is null, are
actually much less than Welsh and Knight presented. In any case, when such
discoveries are presented as "possibly" substantial (and therefore
also possibly trivial), they are arguably not false.

•MBI is actually a Bayesian form of
inference, specifically a "Reference Bayes" method, in which the
conventional confidence interval is combined implicitly with a
non-informative prior belief (Burton et
al., 1998). A non-informative prior is
appropriate, because prior information is usually too vague to quantify in a
trustworthy fashion (Burton et
al., 1998).

•Contrary to what Welsh and Knight
imply at the end of their article, we have not ignored the issues of data
structure, multiple covariates, and the distribution and scale of the outcome
variable. Incredibly, they even imply that we have not attended to effect
size.

•The sample-size estimates in the spreadsheet at Sportscience are correct.
Those for NHST were checked with G*Power (Faul et
al., 2007); the check on MBI sample sizes is
the fact that the chosen Type 1 and 2 errors are equal to the chances of harm
and benefit shown in the spreadsheet for a marginally clear outcome.

•With suboptimal sample sizes,
clear effects are more frequent than statistically significant effects.
Researchers will therefore get more of their studies into print, andpublication
bias will decline, if clear rather
than significant is a criterion for
publication.

•Welsh and Knight suggest that confidence intervals or
a fully Bayesian method (i.e., with an informative prior) should be used to
make inferences, but like everyone else who criticizes NHST, they do not say
how researchers should make decisions about their effects.

We have seen
the response of Welsh and Knight to our letter, but it amounts only to a
denial of our assertions about their mistakes.